Question
In: Statistics and Probability
Use multiple regression with dummies, since the data is seasonal for the regression model. Year Sales...
Use multiple regression with dummies, since the data is seasonal for the regression model.
| Year | Sales (Millions) | Trend |
| 2014 1 | 480.0 | 1 |
| 2014 Q2 | 864.0 | 2 |
| 2014 Q3 | 942.0 | 3 |
| 2014 Q4 | 1,100.0 | 4 |
| 2015 Q1 | 1,200.0 | 5 |
| 2015 Q2 | 1,900.0 | 6 |
| 2015 Q3 | 1,900.0 | 7 |
| 2015 Q4 | 1,300.0 | 8 |
| 2016 Q1 | 1,200.0 | 9 |
| 2016 Q2 | 1,500.0 | 10 |
| 2016 Q3 | 1,200.0 | 11 |
| 2016 Q4 | 500.0 | 12 |
| 2017 Q1 | 356.0 | 13 |
| 2017 Q2 | 1,300.0 | 14 |
| 2017 Q3 | 1,000.0 | 15 |
| 2017 Q4 | 425.0 | 16 |
| 2018 Q1 | 273.0 | 17 |
| 2018 Q2 | 769.0 | 18 |
| 2018 Q3 | 456.0 | 19 |
| 2018 Q4 | 387.0 | 20 |
| 2019 Q1 | 245.0 | 21 |
| 2019 Q2 | 882.0 | 22 |
| 2019 Q3 | 557.0 | 23 |
| 2019 Q4 | 551.0 | 24 |
Solutions
Expert Solution
Result:
Dummy variable is created .
Q1= 1 if it Quarter1 or 0 therwise
Q2= 1 if it Quarter2 or 0 therwise
Q3= 1 if it Quarter3 or 0 therwise
|
Regression Analysis |
|||||||
|
R² |
0.511 |
||||||
|
Adjusted R² |
0.408 |
n |
24 |
||||
|
R |
0.715 |
k |
4 |
||||
|
Std. Error of Estimate |
372.640 |
Dep. Var. |
Sales (Millions) |
||||
|
Regression output |
confidence interval |
||||||
|
variables |
coefficients |
std. error |
t (df=19) |
p-value |
95% lower |
95% upper |
|
|
Intercept |
a = |
1,218.475 |
217.817 |
5.594 |
2.15E-05 |
762.580 |
1,674.370 |
|
t |
b1 = |
-36.284 |
11.135 |
-3.259 |
.0041 |
-59.589 |
-12.979 |
|
Q1 |
b2 = |
-193.685 |
217.722 |
-0.890 |
.3848 |
-649.382 |
262.012 |
|
Q2 |
b3 = |
419.432 |
216.293 |
1.939 |
.0675 |
-33.275 |
872.139 |
|
Q3 |
b4 = |
262.383 |
215.432 |
1.218 |
.2382 |
-188.521 |
713.287 |
|
ANOVA table |
|||||||
|
Source |
SS |
df |
MS |
F |
p-value |
||
|
Regression |
2,757,980.081 |
4 |
689,495.020 |
4.97 |
.0065 |
||
|
Residual |
2,638,352.877 |
19 |
138,860.678 |
||||
|
Total |
5,396,332.958 |
23 |
The estimated regression line is
Sales = 1,218.475-36.284*t -193.685*Q1+419.432*Q2+262.383*Q3
51.1% of variance in sales is explained by the model. F=4.97, P=0.0065, The model is significant.
Data:
|
Year |
Sales (Millions) |
t |
Q1 |
Q2 |
Q3 |
|
2014 Q1 |
480 |
1 |
1 |
0 |
0 |
|
2014 Q2 |
864 |
2 |
0 |
1 |
0 |
|
2014 Q3 |
942 |
3 |
0 |
0 |
1 |
|
2014 Q4 |
1,100.00 |
4 |
0 |
0 |
0 |
|
2015 Q1 |
1,200.00 |
5 |
1 |
0 |
0 |
|
2015 Q2 |
1,900.00 |
6 |
0 |
1 |
0 |
|
2015 Q3 |
1,900.00 |
7 |
0 |
0 |
1 |
|
2015 Q4 |
1,300.00 |
8 |
0 |
0 |
0 |
|
2016 Q1 |
1,200.00 |
9 |
1 |
0 |
0 |
|
2016 Q2 |
1,500.00 |
10 |
0 |
1 |
0 |
|
2016 Q3 |
1,200.00 |
11 |
0 |
0 |
1 |
|
2016 Q4 |
500 |
12 |
0 |
0 |
0 |
|
2017 Q1 |
356 |
13 |
1 |
0 |
0 |
|
2017 Q2 |
1,300.00 |
14 |
0 |
1 |
0 |
|
2017 Q3 |
1,000.00 |
15 |
0 |
0 |
1 |
|
2017 Q4 |
425 |
16 |
0 |
0 |
0 |
|
2018 Q1 |
273 |
17 |
1 |
0 |
0 |
|
2018 Q2 |
769 |
18 |
0 |
1 |
0 |
|
2018 Q3 |
456 |
19 |
0 |
0 |
1 |
|
2018 Q4 |
387 |
20 |
0 |
0 |
0 |
|
2019 Q1 |
245 |
21 |
1 |
0 |
0 |
|
2019 Q2 |
882 |
22 |
0 |
1 |
0 |
|
2019 Q3 |
557 |
23 |
0 |
 orchestra answered 1 year agoRelated SolutionsUse a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data.
Quarter
Year 1
Year 2
Year 3
1
3
6
8
2
2
4
8
3
4
7
9
4
6
9
11
(b)
Use a multiple regression model with dummy variables as follows
to develop an equation to account for seasonal effects in the data.
Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0
otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise.
If required, round your answers to three decimal places. For...
Use the following data to develop a multiple regression model to predict from and . Discuss...Use the following data to develop a multiple regression model to
predict from and . Discuss the output, including comments about the
overall strength of the model, the significance of the regression
coefficients, and other indicators of model fit. y x1 x2 198 29
1.64 214 71 2.81 211 54 2.22 219 73 2.70 184 67 1.57 167 32 1.63
201 47 1.99 204 43 2.14 190 60 2.04 222 32 2.93 197 34 2.15
Appendix A Statistical Tables *(Round...
Run a multiple regression with trend and seasonal; forecast the next 12 months. year Month...Run a multiple regression with trend and seasonal; forecast the
next 12 months.
year
Month
Crates
1999
Jan
20400
Feb
13600
Mar
17000
Apr
30600
May
23800
Jun
17000
Jul
27200
Aug
30600
Sep
34000
Oct
45900
Nov
40800
Dec
30600
2000
Jan
13600
Feb
23800
Mar
30600
Apr
25500
May
27200
Jun
30600
Jul
23800
Aug
47600
Sep
37400
Oct
45900
Nov
44200
Dec
17000
2001
Jan
20400
Feb
13600
Mar
30600
Apr
22100
May
23800
Jun
30600...
What is a fitted value for a multiple regression model and the data that is used...What is a fitted value for a multiple regression model and the
data that is used to create it? Select one.
-It is the difference between the actual value of the response
variable and the corresponding predicted value (regression error)
using the multiple regression model.
-It is a statistic that explains the relationship between
response and predictor variables.
-It is a statistic that is used to evaluate the significance of
the multiple regression model.
-It is the predicted value of...
Problem 2 (25 %) Use multiple regression to estimate a model for the share of the...Problem 2 (25 %)
Use multiple regression to estimate a model for the share of the
shadow economy of the
form:
SHADOWi = β0 + β1INCOMEi + β2UNABLEi + εi
i = 1, …, 28
List the assumptions for the regression model, and explain the
output provided by Excel.
Provide a nice and easy to read output.
How is the R2 and the standard error calculated what is the
interpretation?
What signs are found for the estimated coefficients...
What is the regression model for the data? Is this a good model? Year 2006 =...What is the regression model for the data? Is this a good
model?
Year 2006 = 8,860 Students
2007 = 9,056
2008 = 9,050
2009 = 9,429
2010 = 9,407
2011 = 9,352
2012 = 9,608
2013 = 10,107
2014 = 10,382
2015 = 10,340
2016 = 10,805
2017 = 11,034
2018 = 11,639
The standard project is to use multiple regression analysis to analyze a data set. The data...The standard project is to use multiple regression analysis to
analyze a data set. The data set is a study of student persistent
enrolling in the next semester based on Gender, Age, GPA, a 22
questionnaire on self-efficacy, and student enrollment status.
The educational researcher wants to study the relationship
between student enrollment status as it relates to gender, age,
GPA, and the total response to a 22 questionnaire survey.
a. The estimated multiple regression analysis equation. b. Does
the...
Use the SAT data and create a multiple regression table but this time as input use...Use the SAT data and create a multiple regression table but this
time as input use ONLY two variables: Letters and SAT. Answer
questions 1 to 4. Choose the best fitting answer. Note: numbers are
truncated unless specified.
1. If an incoming student has Letters = 7 and SAT = 1000 what
would his predicted College GPA be?
a. 1.99
b. 2.01
c. 2.18
d. 2.39
2. What is the approximate error of this prediction?
a. 0.58
b. 0.61
c....
Use the SAT data and create a multiple regression table but this time as input use...Use the SAT data and create a multiple regression table but this
time as input use ONLY two variables: Letters and SAT. Answer
questions 1 to 4. Choose the best fitting answer. Note: numbers are
truncated unless specified.
C ollege GPA HighSchl GPA SAT Letters 2.04 2.01 1070 5 2.56 3.4
1254 6 3.75 3.68 1466 6 1.1 1.54 706 4 3 3.32 1160 5 0.05 0.33 756
3 1.38 0.36 1058 2 1.5 1.97 1008 7 1.38 2.03 1104...
Discuss the underlying assumptions of a simple linear regression model; multiple regression model; and polynomial regression.Discuss the underlying assumptions of a simple linear regression
model; multiple regression model; and polynomial regression.
ADVERTISEMENT
ADVERTISEMENT
Latest Questions
ADVERTISEMENT
|